Functions Approximation using Multi Library Wavelets and Least Trimmed Square (LTS) Method

Abdesselem Dakhli, Maher Jbeli, Chokri Ben Amar

2020

Abstract

Wavelet neural networks have recently aroused great interest, because of their advantages compared to networks with radial basic functions because they are universal approximators. In this paper, we propose a robust wavelet neural network based on the Least Trimmed Square (LTS) method and Multi Library Wavelet Function (MLWF). We use a novel Beta wavelet neural network BWNN. A constructive neural network learning algorithm is used to add and train these additional neurons. The general goal of this algorithm is to minimize the number of neurons in the network during the learning phase. This phase is empowered by the use of Multi Library Wavelet Function (MLWF). The Least Trimmed Square (LTS) method is applied for selecting the wavelet candidates from the MLWF to construct the BWNN. A numerical experiment is given to validate the application of this wavelet neural network in multivariable functional approximation. The experimental results show that the proposed approach is very effective and accurate.

Paper Citation

in Harvard Style

Dakhli A., Jbeli M. and Ben Amar C. (2020). Functions Approximation using Multi Library Wavelets and Least Trimmed Square (LTS) Method.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 468-477. DOI: 10.5220/0009802604680477

in Bibtex Style

@conference{iceis20,
author={Abdesselem Dakhli and Maher Jbeli and Chokri Ben Amar},
title={Functions Approximation using Multi Library Wavelets and Least Trimmed Square (LTS) Method},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={468-477},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009802604680477},
isbn={978-989-758-423-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Functions Approximation using Multi Library Wavelets and Least Trimmed Square (LTS) Method
SN - 978-989-758-423-7
AU - Dakhli A.
AU - Jbeli M.
AU - Ben Amar C.
PY - 2020
SP - 468
EP - 477
DO - 10.5220/0009802604680477